Harnessing automation and machine learning to evolve your marketing strategy

David Howlett

Head of Data Science

Digital Marketing Strategy

A growing trend across industries is the use of automation and machine learning. Machine learning automates certain manual processes, such as analysing huge sets of data, which can improve business efficiency and performance, as well as reduce financial costs.

The depth of analysis machine learning can provide makes it possible to gather thousands of micro details about your audience, your target market and everything in between. Through analysing patterns and trends, detailed insights allow you make informed decisions about the most valuable leads and pivot your digital marketing strategy towards motives that matter.

Implementing the likes of revenue management systems, data warehouses and machine learning, you avoid missing out on opportunities such as increasing the price you sell hotel rooms at during periods of high demand or quantify missed revenue caused by out-of-stock products.

A MEMSQL study revealed that 61% of marketers cited artificial intelligence and machine learning as the most significant feature of their data strategy.

Using machine learning to value website users


Focusing on customer quality and leads is the key to evolving your marketing strategy. Machine learning opens the opportunity to build a model that predicts the likely value of a person based on their known behaviours, attributes and actions.

This can include analysing the journey a user takes through your website, the products they engage with and if they have purchased before. From here, you can identify users that are most interested in particular products and place them in audiences for retargeting.

There are many solutions and approaches to take, but an excellent first step is getting your CRM data in a place where it can be accessed for modelling. We use Google BigQuery and follow a process that enables us to join online activity through Google Analytics (GA) with offline data held in a CRM.

Taking this a step further, we implement a soft login solution to the website that enables us to maintain a user’s ID each time they provide identifiable information. For example, a user who requests a brochure will be assigned an ID within the CRM. This same ID is then used each time they identify themselves on the website, meaning GA can join the dots better and we are less reliant on all actions occurring in the cookie window of a singular device.



Utilising personalisation for bespoke, one-to-one experiences


The more data is collected, the more opportunities you have to create personalised online experiences. Moving away from segmenting users with rule-based personalisation, you can now utilise algorithms to deliver one-to-one-like experiences that offer bespoke recommendations and navigation around your site.

It’s possible to harness machine learning to build user profiles and automatically suggest products and messaging based on the patterns and progression of their behaviour. For example, if you purchased baby products today, that data would evolve over time as the algorithm recognises the growth of the child and automatically suggests products to suit this timeline. In 2021, after taking on this model, Amazon sales saw 29% growth during a 12 month period.

Personalisation through machine learning is a highly efficient method of matching the right content to ideal customers. Aligning your strategy to buyers’ habits and behaviours improves customer engagement, grows brand value and relevance, and starts you on a journey toward digital maturity.

Protect your business with automation and algorithms


Automation and machine learning algorithms have the potential to protect your business from large influxes and real-time happenings that can carry significant financial impact.

Already, Google Ads’ smart bidding uses machine learning to adjust bids in real-time to achieve campaign targets. Data-driven attribution is now default in GA4, analysing huge sets of data and deciding how much credit to attribute to each touchpoint in a user journey.

Given the rapid nature of variables that can impact prices, revenue management systems provide updates about any business that comes in and presents opportunities to either increase or lower prices, so you can stay ahead of the competition.

Revenue management systems are often managed by humans, but many decisions are automated to avoid missed opportunities. Lennert De Jong, former Chief Commerical Officer at Citizen M Hotels told us, as part of our podcast, how Citizen M Hotels system saved several large influxes due to events in London. “The system will inform you if something is going on, providing recommendations to increase your price by x amount and ask, do you want to implement this?”

Implementing automated processes creates space for advanced algorithms to perform more efficiently and on a much larger scale.

Now is the time to harness machine learning and automation


As we move into the cookie-less landscape, collecting as much first-party data as possible is key to building a sustainable strategy. Machine learning is growing in relevance, as privacy regulations rise and tracking users is becoming increasingly difficult. On top of automated data processing, machine learning’s scope stretches into all corners of marketing including data-driven insights, decision-making, payments, personalisation and more. The ability to leverage available data by crunching thousands of touchpoints provides a heightened perspective on your performance and brings you closer to your audience’s behaviours.

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